[hadoop读书笔记] 第十五章 sqoop1.4.6小实验 - 数据在mysq和hdfs之间的相互转换

P573 从mysql导入数据到hdfs

第一步：在mysql中创建待导入的数据

1、创建数据库并允许所有用户访问该数据库

mysql -h 192.168.200.250 -u root -p

CREATE DATABASE sqoop;

GRANT ALL PRIVILEGES ON *.* TO 'root'@'%';

或 GRANT SELECT, INSERT, DELETE,UPDATE ON *.* TO 'root'@'%';

FLUSH PRIVILEGES;

查看权限：select user,host,select_priv,insert_priv,update_priv,delete_priv from mysql.user;

2、创建表widgets

CREATE TABLE widgets(id INT NOT NULL PRIMARY KEY AUTO_INCREMENT,
widget_name VARCHAR() NOT NULL,
price DECIMAL(,),
design_date DATE,
version INT,
design_comment VARCHAR());

3、导入测试数据

INSERT INTO widgets VALUES(NULL,'sprocket',0.25,'2010-01-10',,'connect two gizmos');
INSERT INTO widgets VALUES(NULL,'gizmo',4.00,'2009-01-30',,NULL);
INSERT INTO widgets VALUES(NULL,'gadget',99.99,'1983-08-13',,'our flagship product');

第二步：执行sqoop导入命令

sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --table widgets -m 1

缺少mysql连接器

先导入mysql的连接器包

再来执行

发现怎么也连接不上远程mysql数据库，需要授权如下：

GRANT ALL ON *.* TO ''@'192.168.200.123';
grant all privileges on *.* to ""@"192.168.200.123" identified by "密码";

FLUSH PRIVILEGES;
select user,host,select_priv,insert_priv,update_priv,delete_priv from mysql.user;

再来执行一下

还是不行的话，就只能是在sqoop命令中通过--username 和--password来显式的指定用户名和密码连接了

sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --table widgets -m 1 -username root -password mysql密码

在yarn管理台查看到这个任务正在运行（RUNNING）http://hadoop-allinone-200-123.wdcloud.locl:8088/cluster

但是最终还是执行失败

失败原因：物理内存使用了156.8远小于分配的1GB，但是虚拟内存使用2.7超过了默认配置的2.1GB，解决方法：

在etc/hadoop/yarn-site.xml文件中，修改检查虚拟内存的属性为false，如下：

<property>
    <name>yarn.nodemanager.vmem-check-enabled</name>
    <value>false</value>
</property>  

运行继续报错：

解决方法：这个目录没有权限

http://www.oschina.net/question/2288283_2134188?sort=time

保证使用hadoop用户启动集群（因为hadoop的集群的用户是hadoop），并为这个文件夹授权755

再来执行，姐们儿就不信了 。。。哒哒哒。。。终于成功了

后台日志：

[hadoop@hadoop-allinone-- sqoop-1.4.]$ sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --tabgets -m 1 -username root -password weidong
Warning: /wdcloud/app/sqoop-1.4./../hbase does not exist! HBase imports will fail.
Please set $HBASE_HOME to the root of your HBase installation.
Warning: /wdcloud/app/sqoop-1.4./../hcatalog does not exist! HCatalog jobs will fail.
Please set $HCAT_HOME to the root of your HCatalog installation.
Warning: /wdcloud/app/sqoop-1.4./../accumulo does not exist! Accumulo imports will fail.
Please set $ACCUMULO_HOME to the root of your Accumulo installation.
Warning: /wdcloud/app/sqoop-1.4./../zookeeper does not exist! Accumulo imports will fail.
Please set $ZOOKEEPER_HOME to the root of your Zookeeper installation.
// :: INFO sqoop.Sqoop: Running Sqoop version: 1.4.
// :: WARN tool.BaseSqoopTool: Setting your password on the command-line is insecure. Consider us instead.
// :: INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
// :: INFO tool.CodeGenTool: Beginning code generation
// :: INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets` AS t LIMIT
// :: INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets` AS t LIMIT
// :: INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /wdcloud/app/hadoop-2.7.
Note: /tmp/sqoop-hadoop/compile/591fd797fbbe57ce38b4492a1c9a0300/widgets.java uses or overrides a deprecated
Note: Recompile with -Xlint:deprecation for details.
// :: INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/591fd797fbbe57ce381c9a0300/widgets.jar
// :: WARN manager.MySQLManager: It looks like you are importing from mysql.
// :: WARN manager.MySQLManager: This transfer can be faster! Use the --direct
// :: WARN manager.MySQLManager: option to exercise a MySQL-specific fast path.
// :: INFO manager.MySQLManager: Setting zero DATETIME behavior to convertToNull (mysql)
// :: INFO mapreduce.ImportJobBase: Beginning import of widgets
// :: INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.joer.address
// :: INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
// :: INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.
// :: INFO client.RMProxy: Connecting to ResourceManager at hadoop-allinone-200-123.wdcloud.locl/8.200.123:8032
// :: INFO db.DBInputFormat: Using read commited transaction isolation
// :: INFO mapreduce.JobSubmitter: number of splits:
// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1485230213604_0001
// :: INFO impl.YarnClientImpl: Submitted application application_1485230213604_0001
// :: INFO mapreduce.Job: The url to track the job: http://hadoop-allinone-200-123.wdcloud.locl:80213604_0001/
// :: INFO mapreduce.Job: Running job: job_1485230213604_0001
// :: INFO mapreduce.Job: Job job_1485230213604_0001 running in uber mode : false
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map 100% reduce 0%
// :: INFO mapreduce.Job: Job job_1485230213604_0001 completed successfully
// :: INFO mapreduce.Job: Counters: 30
    File System Counters
        FILE: Number of bytes read=
        FILE: Number of bytes written=
        FILE: Number of read operations=
        FILE: Number of large read operations=
        FILE: Number of write operations=
        HDFS: Number of bytes read=
        HDFS: Number of bytes written=
        HDFS: Number of read operations=
        HDFS: Number of large read operations=
        HDFS: Number of write operations=
    Job Counters
        Launched map tasks=
        Other local map tasks=
        Total time spent by all maps in occupied slots (ms)=
        Total time spent by all reduces in occupied slots (ms)=
        Total time spent by all map tasks (ms)=
        Total vcore-milliseconds taken by all map tasks=
        Total megabyte-milliseconds taken by all map tasks=
    Map-Reduce Framework
        Map input records=
        Map output records=
        Input split bytes=
        Spilled Records=
        Failed Shuffles=
        Merged Map outputs=
        GC time elapsed (ms)=
        CPU time spent (ms)=
        Physical memory (bytes) snapshot=
        Virtual memory (bytes) snapshot=
        Total committed heap usage (bytes)=
    File Input Format Counters
        Bytes Read=
    File Output Format Counters
        Bytes Written=
// :: INFO mapreduce.ImportJobBase: Transferred 129 bytes in 38.2028 seconds (3.3767 bytes/sec)
// :: INFO mapreduce.ImportJobBase: Retrieved  records.

查看作业历史服务器以了解MR任务执行详情，发现查看不到，原因是因为没有启动作业历史服务器

启动之：

再来查看下，就可以看到作业历史记录了

http://hadoop-allinone-200-123.wdcloud.locl:19888/jobhistory/job/job_1485230213604_0001

可以看到，sqoop导入数据到hdfs只有map任务而没有reduce任务，map任务数目为1，执行完成数目为1，成功数目为1 ，点击Map链接，查看详细

现在，看看是否真的已经导入了这个数据表

第三步：验证导入结果

可以看到 widgets 表的数据已经导入到了HDFS中

除了导入数据到HDFS中，sqoop在导入时还生成导入源代码.java .jar和.class文件

如果只想生成代码而不导入数据，执行以下命令：

sqoop codegen --connect uri --table 表 --class-name 生成的类名称

第四步：追加数据

--direct：能更快速的从表中读取数据，需要数据库支持，如mysql使用外部工具mysqldump

--append：使用追加数据模式来导入数据

现在，我们在mysql中新插入了一条数据

来执行追加命令

sqoop import --connect jdbc:mysql://192.168.200.250/sqoop --table widgets -m 1 -username root -password weidong --direct --append

执行成功

查看下HDFS中的数据

可以看到，已经追加成功

第五步：将HDFS中的数据导出到mysql

复制表widgets为widgets_copy并清空widgets_copy表数据

执行导出命令

当将密码写在命令行，会为安全造成影响，这时，可以使用参数-P取代 --password

在任务执行时动态的输入密码

Setting your password on the command-line is insecure. Consider using -P instead.

所以命令如下：

 sqoop export 
--connect jdbc:mysql://192.168.200.250/sqoop 
-m 1 
--table widgets_copy 
--export-dir widgets/part-m-00002  
--username root 
-P

Enter password:不会回显字符

成功执行日志信息

[hadoop@hadoop-allinone-- /]$ sqoop export --connect jdbc:mysql://192.168.200.250/sqoop -m 1 --table widgets_copy --export-dir widgets/part-m-00002  --username root -P// :: INFO sqoop.Sqoop: Running Sqoop version: 1.4.
Enter password:
// :: INFO manager.MySQLManager: Preparing to use a MySQL streaming resultset.
// :: INFO tool.CodeGenTool: Beginning code generation
// :: INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets_copy` AS t LIMIT
// :: INFO manager.SqlManager: Executing SQL statement: SELECT t.* FROM `widgets_copy` AS t LIMIT
// :: INFO orm.CompilationManager: HADOOP_MAPRED_HOME is /wdcloud/app/hadoop-2.7.
Note: /tmp/sqoop-hadoop/compile/c66df558e872801e493fbc78458e6914/widgets_copy.java uses or overrides a deprecated API.
Note: Recompile with -Xlint:deprecation for details.
// :: INFO orm.CompilationManager: Writing jar file: /tmp/sqoop-hadoop/compile/c66df558e872801e493fbc78458e6914/widgets_copy.jar
// :: INFO mapreduce.ExportJobBase: Beginning export of widgets_copy
// :: INFO Configuration.deprecation: mapred.job.tracker is deprecated. Instead, use mapreduce.jobtracker.address
// :: INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
// :: INFO Configuration.deprecation: mapred.reduce.tasks.speculative.execution is deprecated. Instead, use mapreduce.reduce.speculative
// :: INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
// :: INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
// :: INFO client.RMProxy: Connecting to ResourceManager at hadoop-allinone-200-123.wdcloud.locl/192.168.200.123:8032
// :: WARN hdfs.DFSClient: Caught exception
java.lang.InterruptedException
    at java.lang.Object.wait(Native Method)
    at java.lang.Thread.join(Thread.java:)
    at java.lang.Thread.join(Thread.java:)
    at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.closeResponder(DFSOutputStream.java:)
    at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.endBlock(DFSOutputStream.java:)
    at org.apache.hadoop.hdfs.DFSOutputStream$DataStreamer.run(DFSOutputStream.java:)
// :: INFO input.FileInputFormat: Total input paths to process : 1（仅处理一个路径的数据导出）
// :: INFO input.FileInputFormat: Total input paths to process :
// :: INFO mapreduce.JobSubmitter: number of splits:
// :: INFO Configuration.deprecation: mapred.map.tasks.speculative.execution is deprecated. Instead, use mapreduce.map.speculative
// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1485230213604_0005
// :: INFO impl.YarnClientImpl: Submitted application application_1485230213604_0005
// :: INFO mapreduce.Job: The url to track the job: http://hadoop-allinone-200-123.wdcloud.locl:8088/proxy/application_1485230213604_0005/
// :: INFO mapreduce.Job: Running job: job_1485230213604_0005
// :: INFO mapreduce.Job: Job job_1485230213604_0005 running in uber mode : false
// :: INFO mapreduce.Job:  map % reduce %
// :: INFO mapreduce.Job:  map 100% reduce 0%
// :: INFO mapreduce.Job: Job job_1485230213604_0005 completed successfully
// :: INFO mapreduce.Job: Counters: 30
    File System Counters
        FILE: Number of bytes read=
        FILE: Number of bytes written=
        FILE: Number of read operations=
        FILE: Number of large read operations=
        FILE: Number of write operations=
        HDFS: Number of bytes read=
        HDFS: Number of bytes written=
        HDFS: Number of read operations=
        HDFS: Number of large read operations=
        HDFS: Number of write operations=
    Job Counters
        Launched map tasks=
        Data-local map tasks=
        Total time spent by all maps in occupied slots (ms)=
        Total time spent by all reduces in occupied slots (ms)=
        Total time spent by all map tasks (ms)=
        Total vcore-milliseconds taken by all map tasks=
        Total megabyte-milliseconds taken by all map tasks=
    Map-Reduce Framework
        Map input records=
        Map output records=
        Input split bytes=
        Spilled Records=
        Failed Shuffles=
        Merged Map outputs=
        GC time elapsed (ms)=
        CPU time spent (ms)=
        Physical memory (bytes) snapshot=
        Virtual memory (bytes) snapshot=
        Total committed heap usage (bytes)=
    File Input Format Counters
        Bytes Read=
    File Output Format Counters
        Bytes Written=
// :: INFO mapreduce.ExportJobBase: Transferred 334 bytes in 30.6866 seconds (10.8842 bytes/sec)
// :: INFO mapreduce.ExportJobBase: Exported 4 records.（导出了4条记录）

可以看见，mysql表已导入数据

至此，mysql和hdfs相互的数据导入导出就完毕了